MusicRecs

A fun way to recommend music based on your similarity to other twitter users.

This is "musicrecsdemo" by Northwestern U. Knight Lab on Vimeo, the home for high quality videos and the people who love them.

What it does

MusicRecs generates music recommendations based on the similarity of a user’s social media profile to other users. It works by maintaining a database of tweets from users who have shared Spotify share links for songs they have listened to. Then, when a user inputs a Twitter handle, the Twitter API is queried to retrieve the last 100 Tweets from that individual, which are then concatenated and compared to the database of other users’ Tweets via a match query through ElasticSearch. The system then returns the users which are most similar to the inputted user and produces playlists with the songs those similar users have listened to on our web page.

How it works

Key Technologies:

● Twitter API

● Parse Database

● ElasticSearch

● Python

● Flask

Next Steps

● Gaining access to the Twitter Firehose to bypass the limit on querying the Twitter API for

Tweets

● Hosting the project on Knight Lab Servers

● Locally hosting the database to improve speed when scale increases

● Perform statistical analysis to determine how well / if how you tweet accurately predicts